how to buy stock in artificial intelligence Guide

This comprehensive guide explains how to buy stock in artificial intelligence, covering exposure routes (individual stocks, ETFs/funds, private/crypto alternatives), subsectors, step-by-step buying...

2025-09-21 01:59:00

By Alex Carter

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How to Buy Stock in Artificial Intelligence

Introduction

how to buy stock in artificial intelligence is a practical question for investors who want equity exposure to companies and vehicles that materially benefit from artificial intelligence — from GPU and semiconductor makers to cloud providers, AI software platforms, and AI-first application firms. This guide explains what that exposure means, common routes (individual stocks, ETFs and mutual funds, index and sector funds), why investors seek AI exposure, and how to take practical steps to buy AI-related securities through a regulated platform such as Bitget.

In the first 10 minutes of reading you will learn: the main AI subsectors and representative public companies to research, how to choose instruments and place orders, key due-diligence items, trading mechanics and tools, risk-management best practices, international and tax considerations, and a one‑page checklist to use before you buy.

As of Dec 24, 2025, according to market reports and company disclosures cited in financial press, leading AI infrastructure firms and cloud providers reported material revenue growth tied to AI adoption and large data center contracts. These developments shape how to buy stock in artificial intelligence today and into the near future.

Why Invest in AI

AI investing is driven by structural changes across compute, data, algorithms, and cloud adoption. The technology acts as an enabling layer for productivity and new product types, prompting secular demand across enterprise software, cloud hosting, data centers, and specialized hardware.

Structural drivers: larger and cheaper compute (GPUs/accelerators), exponentially growing training datasets, improvements in model architectures, and enterprise cloud adoption that turns AI into monetizable services.
Potential benefits: exposure to high-growth markets, broad secular adoption across industries, and the possibility of durable revenue streams (e.g., enterprise SaaS with AI features).
Theme vs single-company bets: investing in AI as a theme (via diversified ETFs/funds) reduces idiosyncratic risk compared with concentrated bets on a single company. However, thematic investments may still be top‑heavy toward large winners.

Understanding how to buy stock in artificial intelligence begins with these trade-offs: growth potential versus concentrated risk and valuation sensitivity.

Ways to Gain Exposure to AI

Individual equities (U.S. and international)

Buying shares in individual companies is the most direct way to gain equity exposure. Candidates include:

Hardware and accelerator providers (GPUs, TPUs, custom ASICs).
Semiconductor manufacturers and memory/storage suppliers.
Cloud and hyperscaler providers that sell AI compute and platform services.
AI platform and tooling companies (model providers, data labeling, MLOps).
Application companies that monetize AI features (vertical SaaS, productivity tools).

Individual equities let investors target specific competitive moats and business models but require more research and carry single-stock risk.

Thematic ETFs and mutual funds

AI-focused ETFs and mutual funds pool many companies that benefit from AI. Typical structures:

Passive ETFs: track an index of AI-related companies (may be sector-weighted).
Active funds: managers select names they believe will outperform.
Factor-based: weight companies by revenue exposure to AI or other metrics.

Advantages: instant diversification within the theme, professional management, and easier execution. Consider expense ratio, holdings concentration, liquidity, and methodology when comparing funds.

Index funds and sector funds

Broader technology or semiconductor index funds provide indirect exposure to AI. These funds reduce single-theme volatility but may dilute pure AI exposure. For example, a broad tech ETF will include legacy software and hardware names alongside AI leaders.

Alternative routes (private equity, venture, and crypto tokens) — brief

Private-stage investing (venture or private equity) and crypto tokens tied to decentralized AI networks offer early-stage or alternative exposure. These differ materially in liquidity, regulatory profile, and risk/return characteristics:

Private investments: higher illiquidity, longer time horizon, accredited-investor restrictions in many jurisdictions.
Crypto tokens: native-network exposure (utility/governance), custody and regulatory considerations, higher volatility.

These are not interchangeable with public equity exposure and suit different investor profiles.

Common AI Subsectors and Examples of Companies

Below are common subsectors and representative public companies to research. These are examples for study, not recommendations.

Compute / hardware: GPU and accelerator providers and system integrators. Representative public examples: Nvidia (leading GPUs), AMD (accelerator GPUs), specialized AI accelerator vendors and integrators.
Semiconductor manufacturing & components: foundries and memory/storage providers (e.g., major memory suppliers and contract manufacturers).
Networking & data center infrastructure: companies supplying networking, power and cooling systems for hyperscale AI data centers.
Cloud & infrastructure: hyperscalers and cloud providers that sell AI compute and platform services; they enable model hosting, inference, and ecosystem integration.
AI platforms & tooling: companies offering model hosting, MLOps, data-labeling, and developer tools that simplify deployment.
Application-layer companies: firms that embed AI into productivity, vertical SaaS, search, and consumer services.
Niche pure-play AI firms: smaller public companies focused primarily on AI products and services.

As of Dec 24, 2025, market coverage reported that some large-cap names had market capitalizations in the trillions and were widely held by institutional investors; this concentration affects index and ETF compositions and should be considered when learning how to buy stock in artificial intelligence.

How to Decide What to Buy — Research & Due Diligence

Business fundamentals

Revenue mix: how much revenue derives from AI-related products and services?
Growth drivers: recurring revenue, enterprise adoption rates, contract cadence.
Customer base: concentration risk (top customers), contract length, and enterprise penetration.
Monetization: direct AI monetization (paid APIs, subscription features) vs indirect (adjacent product upsell).

Technology moat and competitive position

Evaluate whether the company has:

Proprietary models, unique data sets, or specialized silicon that competitors find hard to replicate.
Strong software ecosystems (platform effects), developer communities, and strategic partnerships.
Data advantages that improve model performance and defensibility.

Financial metrics and valuation

Look at:

Revenue growth rate and revenue quality (recurring vs one-time).
Gross and operating margins; R&D intensity and capital expenditures.
Free cash flow and balance-sheet strength.
Valuation multiples relative to growth (P/S, EV/EBITDA, forward P/E).

AI-themed companies often trade at premium multiples; compare valuation to growth and risk profile.

Market adoption and regulatory considerations

Adoption cycles: enterprise procurement lead times vs consumer adoption speed.
Regulatory and ethical risks: privacy rules, model safety, export controls on AI hardware, and potential future regulation of certain AI uses.

These factors feed into how to buy stock in artificial intelligence responsibly and with realistic expectations.

Step-by-Step Guide to Buying AI Stocks (Practical)

Choose a broker or platform

Criteria to consider:

Fees and commission structure, spreads, and non-trading costs.
Market access (U.S. exchanges, international markets), settlement rules.
Fractional shares availability for high-priced stocks.
Research tools, screeners, and educational content.
Custody and security features.

Bitget is available as a regulated trading platform offering global market access and related crypto and wallet products; investors can evaluate Bitget for both equity and crypto-adjacent exposure when selecting a platform.

Fund your account and set an investment plan

Decide allocation to AI exposure in the context of your total portfolio.
Set risk tolerance and time horizon (short-term trading vs long-term buy-and-hold).
Choose lump-sum investment or dollar-cost averaging to manage entry timing.
Consider position sizing rules to limit concentration risk.

Select instruments and place orders

Choose between buying individual shares, ETFs, or mutual funds.
Order types: market orders (instant execution), limit orders (price control), and stop orders.
Use fractional shares if available to allocate precise dollar amounts to expensive names.
Confirm settlement timelines and currency conversions for international purchases.

Post-purchase actions

Monitor your holdings and watch for changes in fundamentals.
Set alerts for earnings, major product announcements, or regulatory shifts.
Establish rebalancing rules or target allocations; consider stop-loss or take-profit frameworks consistent with your risk plan.

Trading Mechanics and Tools

Order types and execution

Market order: execution at current market price — immediate but may suffer slippage.
Limit order: executes only at the specified price or better — protects price but may not fill.
Stop orders (stop-loss or stop-limit): help automate exits, particularly for volatile AI stocks.

Volatile AI names can gap; use limit and stop settings to control execution risk.

Fractional shares and DRIPs

Fractional shares allow investors to buy partial shares of high-priced stocks, improving accessibility and allocation precision.
Dividend reinvestment plans (DRIPs) automatically reinvest dividends into more shares; useful for compounding if the security pays dividends.

Using analysts’ research, screeners, and model portfolios

Use thematic screeners (AI revenue exposure, R&D intensity, data-center sales) to find candidates.
Analyst reports and ETF fact sheets provide quantitative exposure metrics.
Model portfolios and watchlists can help track sector leadership and rebalance decisions.

Bitget’s research and platform tools can be one source among others to screen and monitor AI-related instruments.

Risk Management and Common Pitfalls

Concentration risk: avoid overexposure to a single issuer or a single subsector (e.g., GPUs only).
Hype and bubble risk: AI receives media attention that can inflate valuations; separate fundamentals from sentiment.
Valuation risk: high-growth companies can trade at lofty multiples sensitive to execution surprises.
Execution risk: order type and timing matter for volatile stocks; illiquid names can have wide spreads.
Technology obsolescence: rapid technical change can erode competitive advantages.

Best practices: diversify across subsectors and instruments, limit position sizes, use stop-loss discipline where appropriate, and conduct periodic re-evaluation against your investment thesis.

Portfolio Strategies for AI Exposure

Core-satellite approach

Core: hold diversified broad-market ETFs or large-cap index funds as the portfolio foundation.
Satellite: add AI-themed ETFs or individual AI stocks to boost exposure without excessive concentration.

Thematic-only vs. diversified approach

Thematic-only: higher upside (and risk) if the theme outperforms; higher volatility.
Diversified approach: smoother returns and lower idiosyncratic risk but potentially lower upside from concentrated winners.

Time horizon and rebalancing

Longer horizons allow riding out volatility; short horizons require stricter risk controls.
Rebalance on a schedule (quarterly/semiannual) or when allocations drift beyond set thresholds.

Decide the approach that aligns with financial goals, risk tolerance, and liquidity needs.

Tax, Fees, and Regulatory Considerations

Taxes: capital gains and dividend taxes depend on jurisdiction and holding period (short-term vs long-term). Check local rules and retain records for reporting.
Fund expense ratios: ETFs and mutual funds charge annual fees that reduce net returns; compare expense ratios across similar products.
Brokerage fees: commissions, FX conversion costs, and inactivity fees may apply.
International investors: withholding on dividends, different tax treaties, and broker access rules can affect net returns and logistics.

Regulatory trends affecting AI companies include export controls on advanced chips, data-privacy rules, and possible sector-specific regulation; monitor regulatory developments as they can materially affect business models.

Special Considerations for International Investors

Access to U.S. exchanges: use brokerages that provide ADRs or direct market access.
ADRs: American Depositary Receipts let non‑U.S. companies list in the U.S. market; watch ADR-specific fees and secondary listing norms.
Currency risk: returns denominated in another currency carry FX risk; consider hedged products if appropriate.
Market hours and settlement: coordinate trading windows and settlement cycles across markets.

Bitget and other global brokers may offer international access and custody options; confirm local regulations and tax reporting obligations before trading.

Frequently Asked Questions

Q: Can I buy fractional shares? A: Many brokers and platforms support fractional shares; check your broker’s product list. Fractional shares improve access to high-priced AI leaders.

Q: Are there AI ETFs? A: Yes. There are ETFs that focus on AI, semiconductors, and cloud infrastructure. Review fund fact sheets and holdings for exact exposure.

Q: Should I buy the biggest AI winners or smaller pure plays? A: That depends on risk tolerance. Large winners often offer scale and profitability; smaller pure plays may offer higher growth but higher risk.

Q: How to avoid AI hype? A: Focus on fundamentals: revenue sources tied to AI, customer contracts, margin sustainability, and realistic adoption timelines. Use diversified funds if uncertain.

Due Diligence Checklist (Practical One-Page)

Business model: clear description and how AI contributes to revenue.
Revenue mix: percent from AI-related products, recurring revenue share.
Customers: number of large customers, concentration, contract duration.
Margins: gross and operating margins and recent trends.
Competitive moat: proprietary models, silicon, data, ecosystem.
Management: relevant experience and capital allocation history.
Valuation: multiples vs growth; relative to peers and sector.
Catalysts: product launches, large contracts, partnerships, or regulatory approvals.
Downside scenarios: technology obsolescence, regulatory changes, customer loss.
Liquidity and trading costs: average volume, spread, and platform fees.

Use this checklist before initiating a position.

Representative Public Resources and Further Reading

For ongoing research consult neutral, reputable sources such as ETF fact sheets, company investor relations pages, broker research, and industry overviews. As of Dec 24, 2025, market coverage and data providers reported significant flows into semiconductors and AI infrastructure, which can be checked in fund filings and public company disclosures. Authoritative investor guides and educational sites offer updated explainers and tools.

Suggested resource types (no direct links):

ETF prospectuses and fund fact sheets.
Company quarterly and annual reports (10-Q/10-K) and investor presentations.
Reputable financial news outlets and market-data providers for market cap, volume, and recent corporate deals.
Research reports from broker-dealers and independent analysts.

Glossary

ETF: exchange-traded fund — a basket of securities that trades like a stock.
Expense ratio: the annual fee charged by a fund expressed as a percentage of assets.
GPU/TPU: graphics processing unit / tensor processing unit — hardware accelerators used in AI workloads.
Market cap: market capitalization — share price multiplied by shares outstanding.
Fractional share: a portion of a single share allowing smaller-dollar investments.
Dollar-cost averaging: investing a fixed amount on a regular schedule to smooth entry price.
Limit order: an order to buy or sell at a specified price or better.
Thematic investing: investing based on a long-term structural theme (e.g., AI).

Final notes and next steps

how to buy stock in artificial intelligence starts with a clear plan: set your allocation, pick instruments that fit your risk profile (individual stocks for concentrated exposure; ETFs for diversification), and use a reliable platform such as Bitget to execute and monitor positions. Keep a checklist handy, stay updated on earnings and regulatory developments, and review your holdings periodically.

For practical next steps: open and fund an account on a platform you trust, build a watchlist of AI subsector leaders and funds, and run the due-diligence checklist before placing your first order. Explore Bitget’s platform tools and Bitget Wallet for any crypto‑adjacent or tokenized AI exposure you may consider.

As of Dec 24, 2025, according to market reports and company disclosures, AI infrastructure and semiconductor demand remained a key driver of corporate growth for many large-cap technology firms and for related ETFs; track updated filings and fund fact sheets for current exposure data.

Start your research, apply the checklist, and proceed with a plan that suits your time horizon and risk tolerance. Explore more Bitget features to streamline research and execution.

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